Search Results (2,264)

Gastrointestinal (GI) parasitic infections pose a formidable global challenge to livestock production and continue to affect livestock health and productivity, particularly in tropical and subtropical regions. This study investigated the prevalence, diversity, and epidemiological determinants of GI parasites in 1406 cattle, goats, and sheep from three districts of North Bengal, India (Cooch Behar, Alipurduar, and Jalpaiguri). Parasitological data were analysed using descriptive statistics and inferential methods. Overall prevalence was 69.4%, with cattle showing the highest infection rate (71.62%), followed by sheep (69.30%) and goats (67.19%). Spatial variation was evident among districts, with Cooch Behar recording the highest prevalence (71.20%). Seasonal effects were assessed using Generalized Linear Mixed Models (GLMs), which indicated significantly higher infection probabilities during the monsoon (75.70%) and summer (72.95%) compared with winter (57.78%). The predominant parasite genera identified were Eimeria spp., Strongyloides spp., and Fasciola spp. Host-parasite associations were further explored using Multiple Correspondence Analysis (MCA), revealing distinct clustering patterns, with cattle associated mainly with Eimeria spp. and Strongyloides spp., goats with Trichuris spp. and Nematodirus spp., and sheep with Fasciola spp. and Paramphistomum spp. A species-specific heatmap was used to visualize parasite distribution across host species and seasons, highlighting higher infection intensities during the summer and monsoon periods. Overall, the results demonstrate that GI parasitic infections in North Bengal are influenced by host species and seasonal climatic factors, supporting the implementation of targeted, species- and season-adapted parasite management strategies. Full article

Accurate and stable mapping of crop types is fundamental to agricultural monitoring and food security. However, inter-annual phenological shifts driven by variations in air temperature, precipitation, and sowing dates introduce systematic changes in the spectral distributions associated with the same day of year (DOY). As a result, the “date–spectrum–class” mapping learned during training can become misaligned when applied to a new year, leading to increased misclassification and unstable performance. To tackle this problem, we develop TABS-Net (Temporal–Spectral Attentive Block with Space–Time Fusion Network). The core contributions of this study are summarized as follows: (1) we propose an end-to-end 3D CNN framework to jointly model spatial, temporal, and spectral information; (2) we design and embed CBAM3D modules into the backbone to emphasize informative bands and key time windows; and (3) we introduce DOY positional encoding and temporal jitter during training to explicitly align seasonal timing and simulate phenological shifts, thereby enhancing cross-year robustness. We conduct a comprehensive evaluation on a Cropland Data Layer (CDL) subset. Within a single year, TABS-Net delivers higher and more balanced overall accuracy, Macro-F1, and mIoU than strong baselines, including 2D stacking, 1D temporal convolution/LSTM, and transformer models. In cross-year experiments, we quantify temporal stability using inter-annual robustness (IAR); with both DOY encoding and temporal jitter enabled, the model attains IAR values close to one for major crop classes, effectively compensating for phenological misalignment and inter-annual variability. Ablation studies show that DOY encoding and temporal jitter are the primary contributors to improved inter-annual consistency, while CBAM3D reduces crop–crop and crop–background confusion by focusing on discriminative spectral regions such as the red-edge and near-infrared bands and on key growth stages. Overall, TABS-Net combines higher accuracy with stronger robustness across multiple years, offering a scalable and transferable solution for large-area, multi-year remote sensing crop mapping. Full article

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m³/m³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article

Gastrointestinal nematodes, particularly Haemonchus contortus, represent a major threat to ruminant health and productivity worldwide, largely due to the widespread emergence of anthelmintic resistance. In Bosnia and Herzegovina, benzimidazole resistance has previously been confirmed in domestic ruminants; however, data on wildlife remain lacking. Given the frequent spatial and temporal overlap between domestic and wild ruminants on shared pastures, this study aimed to investigate the occurrence of benzimidazole-resistant H. contortus genotypes within a multi-host system. During the 2024/2025 season, a total of 111 abomasal samples were collected from sheep (n = 20), lambs (n = 12), goats (n = 17), roe deer (n = 40) and chamois (n = 22) across four localities in Bosnia and Herzegovina (Laktaši, Banja Luka, Modriča and Višegrad). Adult H. contortus specimens were morphologically identified and confirmed using real-time quantitative PCR (rt-qPCR). Benzimidazole resistance was assessed by allele-specific rt-qPCR targeting the F200Y mutation in the β-tubulin isotype 1 gene. Statistically significant interspecies differences in β-tubulin genotype distribution were observed (p < 0.05), primarily driven by variation in the homozygous resistant (RR) genotype. High RR prevalence was detected in sheep (60%), lambs (50%) and roe deer (52.5%), whereas lower proportions were observed in chamois (27.3%) and goats (23.5%). Overall, 44.1% of all analyzed H. contortus isolates carried homozygous resistant alleles, indicating an advanced stage of benzimidazole resistance within this multi-host system. These findings demonstrate that benzimidazole resistance in H. contortus is not confined to domestic livestock but is also present in wild ruminants sharing the same grazing areas, consistent with circulation of resistant parasites within shared grazing systems. Full article

Outdoor recreational activities, particularly cycling and mountain biking, are rapidly expanding in forested landscapes, raising concerns about their effects on wildlife. Although bike trails are increasingly common, their ecological impacts on large mammals remain insufficiently studied. We investigated how bike trail use influences the abundance and spatial behaviour of roe deer (Capreolus capreolus) and wild boar (Sus scrofa) in three contrasting forest environments in the Czech Republic. We surveyed roe deer raking and bedding sites and wild boar rooting along 734 transects positioned perpendicular to bike trails, monitored cyclist activity using automated counters, and recorded habitat characteristics. Generalized linear mixed models were used to evaluate the effects of trail proximity, cycling intensity, and vegetation structure. Cycling intensity did not influence overall species abundance; however, roe deer consistently avoided resting close to trails, leading to a measurable loss of potential resting habitat. Roe deer raking decreased with higher cycling intensity at the most remote site, while wild boar rooting was driven primarily by vegetation structure. These findings demonstrate that even low-intensity recreation can alter wildlife behaviour. We recommend maintaining unmanaged buffer zones along trails to provide refuge and reduce disturbance. Our results offer guidance for sustainable trail planning in forest ecosystems. Our conclusions are based on sign surveys collected during one growing season and quantify spatial responses up to 100 m from trails; diel activity, detectability, and seasonal variation were not directly assessed. Full article

Gravity waves (GWs) are an important dynamic process in the planetary atmosphere. They are typically excited by convection, topography, or other sources from the lower atmosphere and propagate upwards. The GWs have a significant effect on the global atmospheric circulation on Mars. However, the lack of high-resolution data from previous observations has resulted in an insufficient understanding of GWs in the Martian atmosphere, particularly in terms of its global distribution and long-term evolution characteristics at different altitudes. Based on multiple years of Mars Climate Sounder (MCS) limb observations on board the Mars Reconnaissance Orbiter (MRO), we conducted a detailed study of the global distribution, seasonal and interannual variations in Martian atmospheric GWs with vertical wavelengths ranging from 9 to 15 km at three different altitude ranges, i.e., the low-altitude range of 200–20 Pa (Lp, ~10–30 km), the mid-altitude range of 20–2 Pa (Mp, ~30–50 km), and the high-altitude range of 2–0.2 Pa (Hp, ~50–70 km). The results indicate complex regional and north–south differences, as well as night–day variations, in the spatial distribution of GWs. Particularly, a three-wave structure of the GW activity is observed over mountainous regions in the mid-to-low latitudes of the Northern Hemisphere. The peak longitude range of this structure closely matches the mountainous terrain. In addition, our results reveal the presence of bands of GW aggregations in the mid- to-high latitudes of the Northern Hemisphere in the Mp and Hp layers, which may be caused by the instability of the polar jet. There are also obvious seasonal and interannual variations in GW activities, which are related to topography, polar jets, and large dust storms. The interannual variations in GWs imply that, in addition to the well-known large seasonal dust storms, complex interannual variations in atmospheric activity over the polar jets and in the complex topography at mid-to-low latitudes on Mars may also exist, which deserve further studies in the future. Full article

Soil degradation and limited root-exploitable depth restrict maize productivity in Plinthosols of tropical regions. However, the combined effects of subsoiling and amendments derived from termite mound materials on soil functionality and yield remain insufficiently quantified. This study examines how variations in a functionally exploitable rooting depth, within a management system combining subsoiling and termite mound amendments, are associated with soil physicochemical properties and spatial variability of maize (Zea mays L.) grain yield in the Lubumbashi region of the Democratic Republic of the Congo. Spatial soil sampling and correlation analyses were used to identify the dominant pedological factors controlling yield variability. The results indicate a reduced vertical stratification of most nutrients within the explored depth, reflecting a more homogeneous distribution of soil properties within the managed profile, although direct causal attribution to specific practices cannot be established in the absence of untreated control plots. Improved rooting conditions were reflected by high and spatially variable productivity (2.3 to 11.1 t ha⁻¹ across blocks), accompanied by a moderate average gain between seasons (<1 t ha⁻¹), while extractable manganese emerged as a consistent negative predictor of yield. These patterns are consistent with a larger functionally exploitable rooting depth and an improved soil environment, although causal contributions of subsoiling and termite mound amendments cannot be isolated in the absence of control plots. Overall, the results highlight the importance of jointly considering structural and chemical soil properties when interpreting productivity gradients in Plinthosols and designing sustainable management strategies for degraded tropical soils. Full article

Relative humidity (RH) is an important meteorological factor that affects both the climate system and human activities. However, the existing observational station data are insufficient to meet the requirements of regional scale research. Machine learning methods offer new avenues for high precision RH estimation, but the performance of different algorithms in complex geographical environments still needs to be thoroughly evaluated. Based on Chinese observational station data from 2011 to 2020, this study systematically evaluated the performance of three methods for estimating RH: the generalized linear mixed model (GLMM), random forest (RF) and the XGBoost algorithm. The results of ten-fold cross validation indicate that the two machine learning methods are significantly superior to the traditional GLMM. Among them, RF performed the best (the determinant coefficient (R²) = 0.73, root mean square error (RMSE) = 8.85%), followed by XGBoost (R² = 0.72, RMSE = 9.07%), while the GLMM performed relatively poorly (R² = 0.58, RMSE = 11.08%). The model performance shows significant spatial heterogeneity. All models exhibit high correlation but relatively large errors in the northern regions, while demonstrating low errors yet low correlation in the southern regions. Meanwhile, the model performance also shows significant seasonal variations, with the highest accuracy observed in the summer (June to September). Among all features, dew point temperature ($T_d$) aridity index (AI) and day of year (DOY) are the main contributing factors for RH estimation. This study confirms that the RF model provides the highest accuracy in RH estimation. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

The region of South-East Europe, located in geologically and climatically diverse areas, hosts a wide range of freshwater habitats. However, comprehensive studies of macroinvertebrate communities are limited, and research on Chironomidae (Diptera) is particularly scarce. We present data on the diversity and structure of chironomid assemblages in hilly and mountainous streams across three ecoregions: the Pannonian Lowland (Ecoregion 11), the Dinaric Western Balkans (Ecoregion 5), and the Eastern Balkans (Ecoregion 7) and provide a comparative overview of their community patterns based on 130 samples. According to the CCA results and Monte Carlo permutation tests, water temperature, dissolved oxygen, conductivity, pH, and altitude were identified as statistically significant parameters influencing Chironomidae assemblages across the ecoregions, collectively explaining 72.20% of the variation. The higher diversity indices were recorded in each season in the Pannonian Lowland and the highest within-ecoregion similarity. Dissimilarity was highest between ER11 and ER7 and lowest between ER5 and ER7. These results demonstrate that the ecoregion was the strongest influence of the studied environmental variables on Chironomidae assemblages, with community patterns closely reflecting their spatial distribution across distinct ecoregional settings. Full article

The Pacific sleeper shark (Somniosus pacificus) is a long-lived, deep-water, sub-polar species that exhibits flexible foraging strategies, likely combining scavenging with active predation on a broad range of prey, yet their role in marine food webs and impact on commercial species remain undetermined. Tracking the location of Pacific sleeper sharks in Alaskan coastal waters is extremely challenging given the predominantly aphotic depths that these sharks occupy, often in spatially constrained and critically under-sampled regions: deep, steep-flanked, convoluted fjords of Prince William Sound (PWS). From the first ever, year-long depth and temperature records recovered from archiving pop-up satellite-linked transmitters (n = 7), we characterized the residence distributions, depth, and thermal habitat for sharks within the PWS fjords and identified seasonal and temporal variation in habitat use. Depths recorded from the seven sharks ranged from 3 to 572 m, and pop-up tag locations suggested a high degree intra-annual residency within western PWS. Ambient water temperatures ranged from 2.65 to 11.1 °C, with little deviation from the median of 5.9 °C. Seasonal patterns emerged within and across individuals relative to the variation in vertical movements, ambient temperatures, and horizontal movements that could reflect resource-oriented strategies. The high degree of residency combined with extensive use of the water column facilitates the use of physically recoverable, high-resolution behavioral and environmental samplers on Pacific sleeper sharks. This adaptive sampling using Pacific sleeper sharks as platforms of opportunity may in turn enable the use of Pacific sleeper sharks as climate and ecosystem sentinels. Full article

Water exchange capacity is critical for maintaining marine environmental quality and supporting the sustainable development of aquaculture. This study applies a high-resolution three-dimensional FVCOM hydrodynamic model coupled with the DYE-RELEASE module. The model was validated against tidal, current, and thermohaline observations. Water residence time (Tre) was used as the primary evaluation metric, supplemented by analyses of residual circulation, material diffusion, and regional variability, to systematically quantify the water exchange mechanisms and seasonal variations in the coastal waters of Changhai County under the combined influence of tides, wind forcing, and thermohaline conditions. Results show that overall residual currents in Changhai County are weak (average velocity: 0.032 m s⁻¹). However, local circulations and stagnation zones frequently develop near islands and channels, strongly influencing material diffusion. In summer, water exchange is primarily controlled by thermohaline effects, which strengthen density stratification, suppress vertical mixing, and modify circulation patterns, thereby reducing the efficiency of tide-driven exchange. Water exchange is weakest near Guanglu Island (46.6–48.6 d) and strongest near Haiyang Island (13–14 d). In winter, wind forcing dominates, enhancing vertical mixing and accelerating water renewal. Residence time in the Changshan Archipelago–Guanglu Island region decreases by 30–50% compared with summer. Overall, winter water renewal is 15–25% more efficient than in summer. This study demonstrates that water exchange in Changhai County is regulated by the combined effects of tides, wind forcing, and thermohaline dynamics. The identified spatial heterogeneity and seasonal characteristics provide a scientific basis for optimizing aquaculture planning and mitigating marine environmental risks. Full article

As one of China’s major megacities, Chongqing is highly vulnerable to rainfall-induced landslides, and the increasing frequency of extreme rainfall driven by climate change further exacerbates risks to infrastructure and public safety. Although numerous studies on landslide susceptibility, quantitative assessments of future landslide frequency under different climate scenarios remain insufficient. This study addresses this gap by integrating high-resolution climate projections with a landslide early-warning model to predict spatiotemporal variations in landslide hazard across Chongqing. Based on regional climate characteristics, the rainy season was divided into three periods: May–June, July, and August–September. Soil moisture variations, together with static geological and topographic factors, were integrated using the information value model to assess the semi-dynamic landslide susceptibilities. On this basis, a regional warning model was then established by linking rainfall thresholds to four geological subregions. High-resolution NEX-GDDP-CMIP6 projections and historical ERA5 0rainfall data were used to quantify changes in exceedance days under four shared socioeconomic pathways (SSPs) from 2021 to 2100. Results indicate a substantial increase in days exceeding the 30% landslide-triggering rainfall threshold, with maximum relative growth of 15.57%. Landslide frequency exhibits pronounced spatial and temporal heterogeneity: increases are observed in May–June and August–September, whereas July trends vary with radiative forcing-decreasing under low-forcing scenarios (SSP1-2.6, SSP2-4.5) and increasing under high-forcing scenarios (SSP3-7.0, SSP5-8.5). The largest increase in frequency reaches 72%, primarily affecting southwestern and central Chongqing. By linking climate projections with rainfall thresholds and semi-dynamic susceptibility assessment, the framework provides a scientific reference for landslide risk prevention and mitigation under future climate scenarios, and offers transferable insights for other mountainous urban regions facing similar hazards. Full article

The vulnerability of reservoirs in Mediterranean regions to eutrophication is attributable to two key factors: strong seasonal hydrological variability and intensive agricultural activity. The present study evaluated the trophic state of 47 reservoirs in the Ebro Basin in Spain using two complementary approaches: the Organisation for Economic Co-operation and Development (OECD) classification system and the criteria set out in Royal Decree (RD) 47/2022. Chlorophyll-a, total phosphorus and transparency data were monitored from 2023 to 2024. While most of reservoirs were classified as oligotrophic to mesotrophic under the OECD thresholds, the RD 47/2022 identified 87% as being at risk of eutrophication. A significant variation in transparency was observed among the different reservoir types (p < 0.05), with high-altitude systems showing higher levels of water transparency. However, chlorophyll-a and total phosphorus had a significant spatial variability, exhibiting only modest correlations. Chlorophyll-a was weakly but significantly correlated to transparency (r = −0.21), while total phosphorus was not significantly associated with either variable, suggesting a decoupling between nutrient availability and phytoplankton biomass. The observed discrepancy between the two classification frameworks is indicative of divergent conceptual approaches (ecological condition versus management risk). It underscores the requirement for integrated monitoring that incorporates chemical, biological and catchment-scale indicators. These findings offer new insight into the trophic dynamics of Mediterranean reservoirs and highlights the importance of adapting regulatory assessment methods to region-specific climatic and hydrological contexts. Full article